Frustum Feature Network.
The purpose of the frustum feature network is to project image information into 3D space, by associating image features to estimated depths. Specifically, the input to the frustum feature network is an image \(\mathbf {I} \in \mathbb {R}^{W_I \times H_I \times 3}\) , where \(W_I, H_I\)  are the width and height of the image. The output is a frustum feature grid \(\mathbf {G} \in \mathbb {R}^{W_F \times H_F \times D \times C}\) , where \(W_F, H_F,\)  are the width and height of the image feature representation, \(D\)  is the number of discretized depth bins, and \(C\)  is the number of feature channels. We note that the structure of the frustum grid is similar to the plane-sweep volume used in the stereo 3D detection method DSGN [1]}.
